On novelty driven evolution in Poker
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gp-bibliography.bib Revision:1.7970
- @InProceedings{Bonson:2016:SSCI,
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author = "J. P. C. Bonson and A. R. McIntyre and M. I. Heywood",
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booktitle = "2016 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "On novelty driven evolution in Poker",
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year = "2016",
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abstract = "This work asks the question as to whether `novelty as
an objective' is still beneficial under tasks with a
lot of ambiguity, such as Poker. Specifically, Poker
represents a task in which there is partial information
(public and private cards) and stochastic changes in
state (what card will be dealt next). In addition,
bluffing plays a fundamental role in successful
strategies for playing the game. On the face of it, it
appears that multiple sources of variation already
exist, making the additional provision of novelty as an
objective unwarranted. Indeed, most previous work in
which agent strategies are evolved with novelty
appearing as an explicit objective are not rich in
sources of ambiguity. Conversely, the task of learning
strategies for playing Poker, even under the 2-player
case of heads-up Limit Texas Hold'em, is widely
considered to be particularly challenging on account of
the multiple sources of uncertainty. We benchmark a
form of genetic programming, both with and without
(task independent) novelty objectives. It is clear that
pursuing behavioural diversity, even under the heads-up
Limit Texas Hold'em task is central to learning
successful strategies. Benchmarking against static and
Bayesian opponents illustrates the capability of the
resulting Genetic Programming (GP) agents to bluff and
vary their style of play.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI.2016.7849968",
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month = dec,
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notes = "Also known as \cite{7849968}",
- }
Genetic Programming entries for
J P C Bonson
Andrew R McIntyre
Malcolm Heywood
Citations